Choosing which transformations to apply, when to do so, and how often, is thus paramount to achieving good results. However, it requires knowledge about the underlying distribution of the data that is typically unavailable. Several approaches to learning an augmentation policy or distribution over a fixed set of transformations exist, such as reinforcement learning [1]}, genetic algorithms [2]}, density matching [3]}, [4]}, [5]}, gradient matching [6]}, bi-level optimization [7]}, [8]}, jointly optimizing over transformations using regularised objectives [9]} and variational bayesian inference [10]}. Optimization based methods often require computing gradients with respect to transformations [10]}, [7]}. Moreover, several methods resort to computationally intensive search phases, the optimization of auxiliary models, or additional data, while failing to outperform fixed user-defined augmentation distributions [13]}.
